Assessing the vulnerability of the Mississippi Gulf Coast to coastal storms using an on-line GIS-based Coastal Risk Atlas
Bibliographic record
Abstract
Natural disaster losses in the U.S. have been estimated to be between $10 billion and $50 billion annually, with an average cost from a single major disaster estimated at approximately $500 million. One of the primary factors contributing to the rise in disaster losses is the steady increase in the population of high-risk areas, such as coastal areas. The population in coastal counties represents more than half of the U.S. population, but occupies only about one-quarter of the total land area. Coastal areas are particularly susceptible to the catastrophic impacts of hazards. Between 1992 and 1997, nearly three-quarters of the federally declared disasters in the U.S. occurred in coastal states or territories (Ward and Main, 1998). Efforts to mitigate the effects of coastal hazards can be complicated by insufficient information concerning coastal vulnerability. Vulnerability factors include the geologic nature of the coast, the patterns and characteristics of the built environment, and socio-economic conditions. Providing a better understanding of these factors to allow communities to undertake the most appropriate mitigation strategies provides the rational for developing the Coastal Risk Atlas (CRA). The CRA is under development by the National Oceanic and Atmospheric Administration (NOAA) National Coastal Data Development Center (NCDDC) in collaboration with the NOAA Coastal Services Center (CSC). Its purpose is to deliver an on-line risk/vulnerability atlas for the coastal U.S. using NCDDC information technologies (Stinus et al., 2002) and methodologies proven by the CSC. The project has been initially implemented in two pilot areas, the Mississippi Gulf Coast and Northeast Florida. Based on success and lessons learned, it will be expanded to a larger coastal region, and eventually nationwide. This phased approach enables identification and resolution of technical issues, better identification of necessary data, and determining data inadequacies that could drive future data collection and coastal research initiatives. This paper documents the development of the CRA and its application in the pilot areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".